Mitochondrial Signatures Revealed Panmixia in Lutjanus Argentimaculatus (Forsskål, 1775)

RESEARCH ARTICLE

Mitochondrial signatures revealed panmixia in Lutjanus argentimaculatus (Forsskål, 1775)

Gopalakrishnan A1,Vineesh N1, Shihab Ismail1, MukthaMenon2, Akhilesh KV3, Jeena NS1,

Paulton MP1 and Vijayagopal P1

1. ICAR-Central Marine Fisheries Research Institute, Ernakulam, Kerala, India.

2. ICAR-Central Marine Fisheries Research Institute, Regional Centre, Visakhapatnam, India.

3. ICAR-Central Marine Fisheries Research Institute, Regional Centre, Mumbai, India.

Abstract

Mangrove red snapper, Lutjanus argentimaculatus is a commercially important fish. The genetic stock structure of L. argentimaculatus from Indian waters was identified using mitochondrial ATPase 6 and 8 and Cytochrome b genes. A 842 bp region of ATPase 6/8 genes and 1105 bp region of Cyt b gene were amplified in 120 samples from six different locations along

Indian coast and obtained 58 and 66 haplotypes respectively. The high haplotype and low nucleotide diversity values along with mismatch distribution, Tajma D and Fu’s Fs analysis suggested a genetic bottleneck events or founder effect, with subsequent population expansion in

L. argentimaculatus. Co-efficient of genetic differentiation (FST) values was low and non- significant for both ATPase6/8 gene and Cyt b genes indicating low genetic differentiation in L. argentimaculatus which can be managed as a unit stock in Indian waters.

Keywords: Lutjanus argentimaculatus, Mitochondrial markers, Genetic stock structure, Indian waters.

1. Introduction

Lutjanus argentimaculatus (Forsskål, 1775) commonly known as mangrove jack or mangrove red snapper is most valued food fish along the Indo-Pacific region. L. argentimaculatus is an important market species and a major recreational and commercial species throughout its distributional range. It possess complex life history, the immature fishes inhabits riverine and coastal area where as mature ones inhabit offshore reefs. Spawning occurs throughout the year in lower altitude at 40-50 km offshore area, in relatively deep water, juveniles move to rivers and other coastal habitats (Russell et al. 2003). Adult fishes under take several hundred kilometers migration from coastal habitats to offshore areas (Russell and McDougall, 2005). There is no targeted fishery for L. argentimaculatus in Indian waters, and the species is caught mainly with redfishes and snapper gillnetting, handlines, bottom longlines, and trawls. The species is widely cultured in brackish water ponds and marine cages in the South East Asian countries because of their good refund and adoptability to environmental condition particularly salinities (Chen et al.

1990; Emata et al. 1999). The global capture production of L. argentimaculatus was 13179 tonnes and the global aquaculture production was 5357 tonnes in 2013 (Amorim and Westmeyer 2016).

The concept of the stock is crucial for fisheries and conservation management. The conservation of genetic diversity of our natural resources is beneficial in genetic improvement programs and it is a major concern of 21st century management practices (Muneer et al. 2009).

Concurrently, most fisheries management actions across the world aim to keep optimal sustainability and variable user interests such as recreational and commercial harvest (Sloss et al.

2009). Genetic methods have great potential to distinguish distinct populations or stocks of fish species that cannot be identified by morphomeristic characters (Cadrin et al. 2005). More over genetic stock structure studies are a cheap and rapid method to define biological stocks (Ovenden

et al., 2015). The understanding of stock structure using genetics can help in fixing the appropriate scale for management of the stock on a species-specific basis (Ovenden et al. 2015). A species which shows no genetic stock structure indicates wide distribution which would require a different set and scale of management measures as against that of another species which shows high population structuring. In the latter case, the high structuring indicates limited gene-flow and hence if such a stock is overfished it is highly unlikely that it would recover through migration, and a collapse of the fishery could occur (Primmer, 2006). Genetic stock structure studies have been used for studying stock structure of resources common to two contiguous territorial fishing zones

(Ovenden et al. 2009). In this case, genetic stock structure studies were carried out on four species of sharks which occur in the contiguous territorial seas of Australia and Indonesia. Genetic stock structure studies have been carried out widely for Pacific salmon (Carvalho and Hauser, 1994); mangrove snapper (Ovenden and Street 2003) and Lutjanus malabaricus and L erythropterus

(Salini et al, 2006). Mitochondrial DNA (mtDNA) analysis is being increasingly used in modern population and phylogenetic surveys of organisms (Chauhan and Rajiv 2010; Sukumaran et al.

2016, Vineesh et al. 2016). The high evolution rates of mtDNA coupled with maternal inheritance have made them as a precise useful genetic marker for studying population structure and other population-related questions (Saraswat et al. 2014). Mitochondrial ATPase 6/8 genes considered to be fast evolving (1.3% per million years) and used to detect the population structure, influence of historical process and level of connectivity in fishes (McGlashan and Hughes, 2001; Ovenden and Street, 2003; Vineesh et al. 2016). Mitochondrial Cyt b gene shows intraspecific variations and used to detect the population structure of fishes (Cheng and Lu, 2005; Saraswat et al. 2014).

Lutjanus argentimaculatus, an important candidate species for aquaculture and also possess biphasic life history. The fishes with biphasic life history displayed genetic stock

structuring across its native distributional range (Shaklee et al. 1991; Salini et al. 2004; Yue et al.

2009). In fishes with biphasic life history, where the juveniles and subadults live in estuaries and fresh waters and adults live in the sea, propose the scale of dispersal may be limited and the species may be subdivided. On the other hand, extensive dispersal during adult or larval phases, may leads to genetic homogeneity. Apart from this, commercial fisher target L. argentimaculatus catch from offshore reef where the bigger one available, compared to inshore areas. Therefore, it is important to reveal the genetic structure of the species for conservation and aquaculture practices. The objective of the present study was to assess the population genetic structure of the species, L. argentimaculatus from Indian waters using two mitochondrial regions (Cytochrome b and ATPase

6/8 gene).

2. Materials and Method

2.1. Sample collection and DNA extraction

A total of 120 samples were collected during November 2014 to May 2016 from six different geographical locations (20 samples per locations) along Indian coast (Mumbai,

Maharashtra; Mangalore, Karnataka; Kochi, Kerala; Mandapam, Tamil Nadu; Vishakhapattanam

(Vizag), Andhra Pradesh and Port Blair, Andaman and Nicobar Islands). All the specimens were photographed, tissue collected and preserved in absolute alcohol for further DNA isolation. The total genomic DNA was isolated from the muscle tissue/fin clips using Qiagen kit (DNeasy blood and tissue kit) and the final DNA concentration was estimated by optical density (OD) reading using a spectrophotometer (Eppendorf BioSpectrometer® basic) set at 260 nm.

2.2. Mitochondrial genes amplification and Sequencing.

ATPase 6/8 gene was amplified by PCR (Applied Biosystems) using universal primers

ATP8 2L8331: 5’-AAAGCRTYRGCCTTTTAAGC-3’ and COIII 2H9236: 5’-

GTTAGTGGTCAKGGGCTTGGRTC-3’. PCR conditions include initial denaturation of 950C for

5 min; followed by 30 cycles of 950C for 30 s, annealing at 550C for 30s, primer extension at 720C for 1 min with a final extension of 720C for 10 min (Vineesh et al. 2016). Cytochrome b gene was amplified using the primers L14724: 5’ -GACTTGAAAAACCACCGTTG-3’ and H15915 5’-

CTCCGATCTCCGGATTACAAGAC-3’. PCR conditions include initial heating at 940C for 4 min, followed by 35 cycles of denaturation at 940C for 50 s., annealing at 550C for 1 min, extension

720C for 1min 30 s and a final extension at 720C for 7 min (Cheng and Lu, 2005). PCR

Amplification was carried out in 25µl of reaction mix using Takara readymade master mix. The

PCR products were electrophoresed on 1.5 % agarose gel and stained with Ethidium bromide.

After electrophoresis the product were visualized in the Gel-Doc system (BIO-RAD, Molecular

Imager, Gel DocTM XR) and Molecular weight determined using 100bp DNA markers

(GeNeiTM). The PCR products were then labeled using the Big Dye Terminator V.3.1 Cycle sequencing Kit (Applied Biosystems Inc) and sequenced bi-directionally using ABI 3730 capillary sequencer following the manufacturer’s instructions.

2.3. Sequence analysis, population genetic and demographic analysis

The DNA sequences were edited by using the software BioEdit sequence alignment editor version 7.0.5.2 (Hall 1999). Haplotype number, haplotype frequency (h) and nucleotide diversity

(π) among samples collected from different location were estimated using the software DnaSP 5.0

(Librado and Rozas 2009). All the samples collected from sampling sites of different states considered as a different population and genetic parameters like AMOVA and F statistics, pairwise

FST, mismatch distribution, Tajma D and Fu’s Fs were analyzed using the software ARLEQUIN

ver 3.5 (Excoffier and Lischer 2010). Analysis of molecular variance (AMOVA) was carried out to reveal the difference among the stocks. Here, samples collected from the different sampling sites of each state were considered as a single population, for the next level of analysis samples collected from east coast and west coast of India considered as separate group. This analysis consists of three hierarchical groupings of the data. The first level compared the variation among individuals within each population. The second level examined genetic structure among populations of each group. Finally, variation was determined by combining all geographical samples and F-statistics were calculated to study the population genetic stock structure.

Demographic studies were carried out using the mismatch distribution in ARLEQUIN ver 3.5 and

DnaSP 4.0. Mismatch analysis conducted separately for two major coast; West coast (Maharashtra,

Karnataka and Kerala) and East Coast (Tamil Nadu, Andhra Pradesh and Andaman) (Fig 1).

Tajima's D and Fu's Fs were also estimated to test the deviations from neutrality either due to selection, bottleneck, or population expansion. The number and rate of transitions/transversions were calculated using the program MEGA version 6.0 (Tamura et al. 2013). Raggedness index value (RI) and sum of square deviation (SSD) value also calculated using the software

ARLEQUIN. A minimum spanning haplotype network showing genetic relationships among the haplotypes was constructed using PopART Ver. 1.7 (Leigh and Bryant 2015).

3. Results

A total of 1105 bp sequence of Cyt b gene was obtained from 120 individuals of six different locations. There were 77 polymorphic sites with 66 haplotypes including 32 parsimony informative and 42 singleton variable sites in Cyt b gene. All the haplotypes were submitted to

NCBI GenBank with the accession numbers KU975630-KU975685 and KX590954-KX590963.

Low nucleotide diversity and high haplotype diversity (pi: 0.00326 and Hd: 0.929 respectively) was found for all the samples. The average frequency of four nucleotide were A: 25.3; T: 26.7; C:

33.6 and G: 14.4. A total of 91 transitions and 9 transversion were observed and the estimated

Transition/Transversion bias (R) was 9.958.

A total of 842 bp sequence of ATPase 6/8 gene was amplified from 120 samples of the six locations. ATPase 6/8 gene consist of two fragments, 168 bp fragment of ATPase 8 and 684 bp of

ATPase 6, including an overlapping region of 10 bp from 159 to168bp. A total of 62 polymorphic sites with 58 haplotypes including 30 singleton variable sites and 32 parsimony informative sites were observed. All the haplotypes were submitted to NCBI GenBank with accession numbers

KX590896-KX590953. The mean nucleotide diversity for all the samples was found to be, Pi:

0.00412, whereas haplotype diversity was recorded as, Hd: 0.944. The average frequency of four nucleotide for all samples were A: 26.3; T: 26.6; C: 34.7 and G: 12.3. Total number of transitions and transversions were 97.08 and 2.92 respectively and the estimated transition/transversion bias

(R) was 29.12.

Analysis of molecular variance in L. argentimaculatus showed low level of variance among groups (1.03% for ATPase 6/8 gene and -0.1% for Cyt b gene) and within groups (2.11% for

ATPase gene and 0.62% for Cyt b gene) with high variance within populations (96.86% for

ATPase gene and 99.48% for Cyt b gene.). The genetic divergence among different populations were estimated with pairwise FST ranged from -0.031 to 0.041 for ATPase genes and -0.0033 to

0.023 for Cyt b gene. Both AMOVA and pair wise FST values were low and non-significant.

Tajima's D was negative and significant with mean value -1.859 and -1.66 for Cyt b gene and

ATPase 6/8 gene respectively.. Fu's Fs values were also significantly negative with mean value -

9.103 and -6.845 for Cyt b gene and ATPase 6/8 gene respectively. Significant negative value indicates that there were more nucleotide site variants than the expected under neutral model of evolution (Simonsen et al. 1995). Mismatch distribution curve of both genes (Cyt b and ATPase

6/8) showed a unimodal curve indicating the population is under expansion after recent bottleneck

(Fig 4 and Fig 5). Minimum spanning haplotype network showed the absence of phylogeographic structuring (Fig 2 and Fig 3). The raggedness index value (RI 0.036, P value 0.66 for ATPase genes and RI 0.042, P value 0.59 for cyt b gene) and sum of square deviation (SSD 0.02, p value

0.43 for ATPase gene and SSD 0.012, P value 0.48 for cyt b gene) values were not significant indicating the population is under expansion (Table 1).

4. Discussion

Lutjanus argentimaculatus is a highly prized fish in marine capture fisheries, mariculture and sport fishing throughout its distributional range (Russell and McDougall 2008). Since it is a commercially important species, it is important to reveal the genetic stock structure of the species.

In the present study combined analysis of two mitochondrial markers was used to detect the genetic stock structure and demographic pattern of L. argentimaculatus from east and west coasts of India and this is the first reported work to assess the population diversity of the L. argentimaculatus from this area. Mitochondrial genes such as ATPase 6/8 and Cyt b gene are proven markers for population studies in fishes (Ovenden and Street 2003; Cheng and Lu 2005; Saraswat et al. 2014;

Vineesh et al. 2016). Our mtDNA analysis of L. argentimaculatus in the Northern Indian Ocean conclude three major results (1) no genetic differentiation of the species among the populations,

(2) high haplotype and low nucleotide diversity and (3) sudden demographic expansion. A total of

1105 bp and 842 bp sequence were amplified for Cyt b and ATPase genes respectively. The

ATPase genes contain an overlapping sequence of 10 bp which is similar to the previous studies

(Kathirvelpandian et al. 2014; Divya et al. 2015; Joy et al. 2016).

4.1. Population genetic structure

The AMOVA results of Cyt b and ATPase 6/8 gene analysis indicated no genetic structure among the population groups of the Northern Indian Ocean in the present study. These results were also supported by pair wise FST estimates. A high level of gene flow was also found between Bay of Bengal and Arabian Sea. Ovenden and Street, (2003) reported a high level of gene flow in L. argentimaculatus in Queensland and possibly throughout Australian waters. This high level of gene flow plays a role in the low levels of spatial genetic subdivision. In addition, minimum spanning network analysis did not show distinct geographic separation of haplotypes among population groups for both the genes. These findings are consistent with Joy et al. 2016 (Cobia) and Vineesh et al. 2016 (Spanish mackerel), which found a lack of genetic differentiation and phylogeographic structure among regions off the west and east coasts of India based on mtDNA

ATPase 6/8 variation. The results, together with shared frequent haplotypes, and similar genetic diversity and population parameters among population groups support the panmictic population structuring of L. argentimaculatus.

The gene flow and genetic differentiation of marine species is determined by many factors including spawning behaviour, Planktonic larval duration, dispersal distances and directions

(Froukh and Kochzius 2007). Usually, low genetic differentiation are reported in marine fishes due to their high dispersal potential during their life history stages, attached with the absence of physical barrier to movement between ocean basins (Mandal et al. 2012). Coastal current pattern of Bay of Bengal and Arabian Sea associated with monsoon currents can carry the planktonic

larvae along the coast. Larval duration of L. argentimaculatus ranges from 45-50 days (Leu et al.

2003). The distance marine larvae are physically transported usually increases with larval duration, so species with high pelagic larval durations (PLD) have widespread dispersal in wind-driven surface currents and in subsurface flows (Siegel et al. 2003). The monsoon currents flow between the Arabian Sea and the Bay of Bengal, which might caused the mixing of populations (Shenoi

2010).

4.2. Demographic history and population expansion

Population demography is considered as an important factor when inferring patterns of population structure. Mitochondrial DNA is having an increased rate of genetic drift and a faster approach to equilibrium between drift and migration in populations that do not have highly skewed sex ratios or extreme male polygyny (Birky et al. 1983). The historical demography was measured by using mismatch distributions, neutrality tests (Tajima 1989; Fu 1997), haplotype diversity and nucleotide diversity. The mismatch distribution curve is usually unimodal for populations that experienced a recent demographic expansion (Rogers and Harpending 1992). In the present study for both the genes, the observed pair wise mismatch distributions for all populations of L. argentimaculatus were not considerably different from the expectations predicted under a sudden population expansion model and similar findings were found in cobia (Joy et al. 2016); Spanish mackerel (Vineesh et al. 2016). Harpending’s (1994) raggedness test and SSD (sum of the square deviations) indicating the statistical significance between the two mismatch distributions was also calculated and the non-significant values for these parameters also indicated that the values do not depart from that expected under the model of expansion. Neutrality tests like Tajima D and Fu’s

Fs are considered as the additional measures for tracing population growth expansion.

Significantly negative values of Tajima D and Fu’s Fs indicate an excess of low frequency

polymorphism compared to that expected for null neutral hypothesis while the significantly positive values point out the signatures of genetic subdivision (Delrieu-Trottin et al. 2014).

Significant negative values of Tajima D and Fu’s Fs in this study indicated that the population of

L. argentimaculatus along the coasts of India had experienced recent population expansion.

Similar results were observed in Rachycentron canadum (Joy et al. 2016) and Scomberomorous commerson (Vineesh et al. 2016).

Haplotype diversity and nucleotide diversity are used to quantify the uniqueness of a particular haplotype in a given population (Nei and Tajima 1981). Based on different combinations of haplotype diversity (h) and nucleotide diversity (π) magnitudes of mtDNA sequence, marine fishes can be classified into four categories defined by Grant and Bowen (1998). Based on the above criteria, low nucleotide diversity (π <0.5) and larger haplotype diversity (h<0.5) (category II), hints population bottle neck followed by rapid growth and accumulation of mutations which was found in L. argentimaculatus samples in Indian waters. Similar pattern of nucleotide and haplotype diversities were noted in marine species like Coilia dussumieri (h=0.8211-0.9368, π=0.0012-0.0025) (Kathirvelpandian et al. 2014); Scomberomorous commerson (h=0.809, π=0.0021) (Vineesh et al. 2016) and Rachycentron canadum (h=0.785, π=0.002) (Joy et al. 2016). Mt-DNA has some limitations; mt-DNA represents only a single locus. So we can see only through a single window of evolution and reflects at best only the maternal lineal history (Skibinski, 1994; Magoulas, 1998), which could well vary from that overall of populations or species. So, the interpretation we make on species/population history is likely to be biased, hence more study should be carried out using independent, genomic molecular markers like microsatellite marker to support mtDNA analysis.

Management measures to be designed for conservation and sustainable utilization of the commercially important fish species. The data generated from this study provide a new perspective for the conservation of L. argentimaculatus of the study region especially due to the combined analysis of two mitochondrial markers information that contributes for the first time. The major

findings of this study have important implications in the conservation of this species, especially considering the low level of genetic diversity that may be caused by historical demographic expansion. The results of the present study can be used in the management of this species as a unit stock in Indian waters. Stock structure information inferred from this study would enhance the selective breeding programs in a faster way. This information on genetic variation and stock structure of L. argentimaculatus along Indian coast can be also used for genetic upgradation, aquaculture, and conservation programmes.

5. Acknowledgements

The research project was funded by ICAR project named ICAR Outreach activity on fish genetic stocks. We thank Prathibha Rohit, Mohammed koya, Kathirvel Pandian and Jishnudev for their support in sample collection. Authors deeply thank Reynold Peter , Chirayath Lavina Vincent for their support in various components in this work especially in literature corrections.

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Received 22 February 2017, final revised form 11 July 2017; accepted 13 July 2017

List of Figure

Fig.1. Map showing collection locations of L. argentimaculatus along the Indian coast

Fig 2. Minimum spanning network constructed using mitochondrial ATPase 6/8 gene.

Fig.3. Minimum spanning network constructed using mitochondrial Cyt b gene.

Fig 4. Mismatch distribution curve of Lutjanus argentimaculatus inferred from mitochondrial

ATPase 6/8 gene. X axis indicate pairwise difference and Y axis indicate frequency A) West coast

B) East Coast

Fig 5. Mismatch distribution curve of Lutjanus argentimaculatus inferred from mitochondrial Cyt b gene. X axis indicate pairwise difference and Y axis indicate frequency. A) West Coast B) East

Coast

ATPase 6/8 genes Cyt b gene Tajima's D Fu's FS (P Raggedness Tajima's D Fu's FS (P Raggedness Location (P value) value) SSD (PSSD) index (P value) (P value) value) SSD (PSSD) index (P value) -1.61036 -4.23049 -1.83239 -8.75246 Maharashtra (0.045)* (0.019)* 0.02191(0.12) 0.06307(0.22) (0.015)* (0.000)* 0.00926(0.46) 0.02507(0.9) -2.01768 -3.81512 -1.56796 -10.16032 Karnataka (0.009)* (0.018)* 0.00263(0.93) 0.01629(0.99) (0.043)* (0.000)* 0.00961(0.23) 0.06568(0.22) -1.40283 -8.51112 -1.87296 -7.91848 Kerala (0.055)* (0.000)* 0.01168(0.26) 0.05285(0.46) (0.017)* (0.000)* 0.04159(0.02) 0.06612(0.12) -1.82243 -9.42384 -2.16895 -11.0069 Tamilnadu (0.015)* (0.000)* 0.00298(0.64) 0.02745(0.73) (0.005)* (0.000)* 0.00228(0.86) 0.02989(0.78) Andhra -1.65222 -6.71069 -1.97865 -10.25016 Pradesh (0.029)* (0.002)* 0.00626(0.62) 0.03163(0.72) (0.011)* (0.000)* 0.00253(0.8) 0.05119(0.56) Andaman -1.13572 -8.38436 -1.73782 -6.53086 islands (0.108) (0.000)* 0.07451(0.01) 0.02598(0.89) (0.018)* (0.001)* 0.0085(0.53) 0.01798(0.97)

Table 1. Demographic parameters for ATPase 6/8 gene and Cyt b gene from six different locations of L. argentimaculatus (*P < 0.05)